import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME




def 							UpperCamelCase		(			__lowercase		:					List[Any]      ):
		'''simple docstring'''




		A_						:							Dict      = [
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    'decoder.output_projection.weight',
		    '_float_tensor',
		    'encoder.embed_positions._float_tensor',
		    'decoder.embed_positions._float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(__lowercase		,__lowercase      )




def 							UpperCamelCase		(			__lowercase		:					int      ):
		'''simple docstring'''




		A_  ,     A_						:							Dict      = emb.weight.shape
		A_						:							Optional[Any]      = nn.Linear(__lowercase		,__lowercase		,bias=__lowercase      )
		A_						:							Optional[int]      = emb.weight.data
		return lin_layer




def 							UpperCamelCase		(			__lowercase		:					List[Any]		,__lowercase		:					Optional[int]=None      ):
		'''simple docstring'''




		A_						:							List[str]      = {}
		for old_key in state_dict.keys():
				A_						:							Dict      = old_key
				if "moe_layer.experts." in key:
						if expert_idx is not None:
								A_						:							Union[str, Any]      = key.replace('moe_layer.experts.0'		,f'''ffn.experts.expert_{expert_idx}'''      )
						else:
								A_						:							Dict      = key.replace('moe_layer.experts.'		,'ffn.experts.expert_'      )
				if "gate" in key:
						A_						:							Dict      = key.replace('.moe_layer.gate.wg'		,'.ffn.router.classifier'      )
				if "fc2" and "experts" not in key:
						A_						:							List[Any]      = key.replace('.fc2.'		,'.ffn.fc2.'      )
				if "fc1" and "experts" not in key:
						A_						:							Optional[Any]      = key.replace('.fc1.'		,'.ffn.fc1.'      )
				if ".encoder_attn." in key:
						A_						:							Optional[int]      = key.replace('.encoder_attn.'		,'.cross_attention.'      )
				if "encoder_attn_layer_norm" in key:
						A_						:							Optional[Any]      = key.replace('encoder_attn_layer_norm'		,'cross_attention_layer_norm'      )
				if "final_layer_norm" in key:
						A_						:							Dict      = key.replace('final_layer_norm'		,'ff_layer_norm'      )
				A_						:							List[Any]      = state_dict[old_key]
		return new_dict




def 							UpperCamelCase		(			__lowercase		:					Optional[int]		,__lowercase		:					List[Any]		,__lowercase		:					str		,__lowercase		:					Optional[int]		,__lowercase		:					str = WEIGHTS_NAME      ):
		'''simple docstring'''




		A_						:							List[str]      = []
		A_						:							int      = 0
		os.makedirs(__lowercase		,exist_ok=__lowercase      )

		for expert in range(__lowercase      ):
				A_						:							Optional[int]      = switch_checkpoint_path + f'''-rank-{expert}.pt'''
				if os.path.isfile(__lowercase      ):
						A_						:							Optional[Any]      = torch.load(__lowercase      )['model']
						remove_ignore_keys_(__lowercase      )
						A_						:							List[Any]      = rename_fairseq_keys(__lowercase		,__lowercase      )
						A_						:							Any      = os.path.join(
						    __lowercase		,weights_name.replace('.bin'		,f'''-{len(__lowercase      )+1:05d}-of-???.bin'''      )      )
						torch.save(__lowercase		,__lowercase      )
						sharded_state_dicts.append(expert_state.keys()      )
						total_size += sum([value.numel() for key, value in expert_state.items()]      ) * dtype_byte_size(
						    expert_state[list(__lowercase      )[0]].dtype      )

    # Add the last block
		A_						:							List[str]      = os.path.join(__lowercase		,weights_name.replace('.bin'		,f'''-{len(__lowercase      )+1:05d}-of-???.bin'''      )      )
		A_						:							Any      = torch.load(switch_checkpoint_path + '-shared.pt'      )['model']
		remove_ignore_keys_(__lowercase      )
		A_						:							Tuple      = rename_fairseq_keys(__lowercase		,__lowercase      )
		A_						:							str      = shared_weights['decoder.embed_tokens.weight']
		sharded_state_dicts.append(shared_weights.keys()      )

		# If we only have the shared weights (dummy model/experts saved on the same file)
		if len(__lowercase      ) == 1:
				A_						:							Any      = os.path.join(__lowercase		,__lowercase      )
				torch.save(__lowercase		,__lowercase      )
				return {weights_name: sharded_state_dicts[0]}, None
		else:
				torch.save(__lowercase		,__lowercase      )
		# Otherwise, let's build the index
		A_						:							Tuple      = {}
		for idx, shard in enumerate(__lowercase      ):
				A_						:							str      = weights_name.replace('.bin'		,f'''-{idx+1:05d}-of-{len(__lowercase      ):05d}.bin'''      )
				A_						:							str      = os.path.join(__lowercase		,weights_name.replace('.bin'		,f'''-{idx+1:05d}-of-???.bin'''      )      )
				os.rename(__lowercase		,os.path.join(__lowercase		,__lowercase      )      )
				for key in shard:
						A_						:							Dict      = shard_file

    # Add the metadata
		A_						:							Dict      = {'total_size': total_size}
		A_						:							Dict      = {'metadata': metadata, 'weight_map': weight_map}

		with open(os.path.join(__lowercase		,__lowercase      )		,'w'		,encoding='utf-8'      ) as f:
				A_						:							str      = json.dumps(__lowercase		,indent=2		,sort_keys=__lowercase      ) + '\n'
				f.write(__lowercase      )

		return metadata, index


if __name__ == "__main__":
	_UpperCAmelCase          =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--nllb_moe_checkpoint_path""",
	    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
	    type=str,
	    required=False,
	    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
	)
	parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
	    type=str,
	    required=False,
	    help="""Path to the output pytorch model.""",
	)
	_UpperCAmelCase          =						parser.parse_args()
	_UpperCAmelCase     ,_UpperCAmelCase          =						shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    128,
	    args.dtype,
	)

	_UpperCAmelCase          =						NllbMoeConfig.from_pretrained(
	    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
	)
	config.save_pretrained(args.pytorch_dump_folder_path)
	_UpperCAmelCase          =						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print("""Done""")
	model.save_pretrained(args.pytorch_dump_folder_path)







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCAmelCase          =						logging.get_logger(__name__)

_UpperCAmelCase          =						{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}





class UpperCAmelCase		(   __A   ,			__A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''bit'''
				lowerCamelCase_        =   ['''preactivation''', '''bottleneck''']
				lowerCamelCase_        =   ['''SAME''', '''VALID''']
				def __init__(		self						,      lowercase=3						,      lowercase=6_4						,      lowercase=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,      lowercase=[3, 4, 6, 3]						,      lowercase="preactivation"						,      lowercase="relu"						,      lowercase=None						,      lowercase=3_2						,      lowercase=0.0						,      lowercase=False						,      lowercase=3_2						,      lowercase=1						,      lowercase=None						,      lowercase=None						,      **lowercase						,      ):



						"""simple docstring"""





						super().__init__(**lowercase   )
						if layer_type not in self.layer_types:
								raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types   )}'''   )
						if global_padding is not None:
								if global_padding.upper() in self.supported_padding:
										A_						:							Tuple      = global_padding.upper()
								else:
										raise ValueError(F'''Padding strategy {global_padding} not supported'''   )
						A_						:							Any      = num_channels
						A_						:							Any      = embedding_size
						A_						:							List[Any]      = hidden_sizes
						A_						:							int      = depths
						A_						:							Union[str, Any]      = layer_type
						A_						:							List[Any]      = hidden_act
						A_						:							Tuple      = global_padding
						A_						:							List[str]      = num_groups
						A_						:							int      = drop_path_rate
						A_						:							str      = embedding_dynamic_padding
						A_						:							Dict      = output_stride
						A_						:							Any      = width_factor

						A_						:							int      = ['stem'] + [F'''stage{idx}''' for idx in range(1						,      len(lowercase   ) + 1   )]
						A_  ,     A_						:							List[Any]      = get_aligned_output_features_output_indices(
						    out_features=lowercase						,      out_indices=lowercase						,      stage_names=self.stage_names   )






